Identification of Initial Data

The virtual screening of chemical libraries has become an essential tool for identifying lead compounds.10-14 As stated in Chapter I, virtual screening utilises the similarity principle, which states that structurally similar compounds are more likely to exhibit similar properties.15-18 Through many successful applications, virtual screening has proven to be a rapid and cost effective strategy for evaluating large virtual databases of chemical compounds.19, 20

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Antimalarial drug discovery efforts within the Chemistry Department at The University of Liverpool have predominantly been concerned with the synthesis and testing of compounds around a particular chemotype, to enable a SAR study and subsequent optimisation around the chemotype. Through collaboration with the Liverpool School of Tropical Medicine (LSTM), compounds can be screened against a required assay, the results from which then drive forward further investigation. There therefore exists vast quantities of biological and chemical data waiting to be utilised, and it is this information which can form the ideal starting point of a LBVS study.

With the biochemical target of interest known (Pfbc1), it was necessary to identify suitable compounds for use in virtual screening. At LSTM, compounds are routinely screened for their whole cell growth inhibition, that is, their reported inhibition of the 3D7 CQS parasite. Given that the objective was to find compounds which selectively inhibit Pfbc1, the data for virtual screening needed to have been tested against this assay. Unfortunately however, owing to the difficulties, expense, and time required to obtain sufficient amounts of purified Pfbc1, compounds are not routinely screened against this particular bioassay, and therefore accurate quantitative IC50 values against Pfbc1 were only available for a limited number of compounds.

The compounds which had been tested against Pfbc1, and therefore used during virtual screening are reported in table 2.1, together with their quantitative Pfbc1 activity values and qualitative classifications.21 These compounds varied significantly in structure and activity, with some active in the single nM range (i.e. Freddie-2-aryl), with others up to several µM (i.e. BC029). Whilst compounds such

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as ATOV and GW844520 are known inhibitors of Pfbc1, most of the others were novel structures synthesised at Liverpool. Given that the compounds varied in their activity values so widely, it was necessary to place qualitative cut offs with which to define compounds as either active or inactive. Whilst it is true that all of the compounds exhibited some Pfbc1 inhibition, in order to maximise the enrichment of virtual screening, only the most potent compounds were defined as active. Several of the virtual screening methods employed also required qualitative results, rather than quantitative. Ultimately, compounds were considered active if they had Pfbc1 IC50 values of less than 100 nM, or if they exhibited complete parasite bc1 inhibition in the nM range. Those with IC50 values greater than 100 nM, or which exhibited µM inhibition were considered inactive. Using these constraints, of the nineteen compounds tested against Pfbc1, twelve were active and the other seven inactive.

Table. 2.1 Compounds with known activity against Pfbc1.

Name Compound Pfbc1 IC50 Activity Classification Freddie-2-aryl 40.9 nM Active Freddie-3-aryl 52.6 nM Active Atovaquone 2.7 nM Active Myxothiazol 3.5 ± 0.5 nM Active

89 Stigmatellin 12 ± 1 nM Active GW844520 32 ± 13 nM Active WR249685 (S enantiomer) 3 ± 2 nM Active Floxacrine (racemic) 802 ± 183 nM Inactive Ruan 1 60% inhibition at 1.4 µM Inactive Ruan 2 3.5 nM Active Ruan 4 32% inhibition at 1.4 µM Inactive Ruan 10 62% inhibition at 1.4 µM Inactive Ruan 11 60% inhibition at 1.4 µM Inactive HDQ 25 nM Active DRUG 1 Complete inhibition at 2.8 µM Inactive

90 DRUG 2 Complete inhibition at 281 nM Active DRUG 3 Complete inhibition at 281 nM Active DRUG 6 Complete inhibition at 281 nM Active BC029 9.28 µM Inactive

2.2

Chemical Library

A chemical library is a collection of compounds readily available for use in HTS and virtual screening,22 and can be widely used for the exploration of chemical space.23, 24

As stated in Chapter I, chemical space is the space spanned by all possible molecules, with the total number of possible small drug like molecules that populate chemical space estimated to exceed 1060.25 With such a vast amount of space it is understandable that its exploration has been limited, with only around 60 million small molecules registered with the Chemical Abstracts Service (CAS) as of September 2011.26 Though the systematic exploration of chemical space is possible through in silico databases of virtual compounds,27, 28 it is expected that much of chemical space contains nothing of biological interest, with searches around specific and focussed areas potentially yielding better results.29

Chemical libraries are available from agencies such as the National Cancer Institute (NCI),30 who have structures for hundreds of thousands of compounds. However,

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many other commercial companies also have libraries of varying sizes available. Perhaps one of the most complete, or at least largest chemical library resources is that of ZINC.31 ZINC is a free database of commercially available compounds ready for virtual screening, compiled from purchasable compounds across numerous sources. In its entirety, it currently consists of over thirteen million compounds whose structures and vendor details, as well as other physicochemical properties are readily available for download and use.

Subsets of the full thirteen million compounds exist that are amenable to particular virtual screening needs. For the LBVS work described here the ZINC lead like library32 of compounds was chosen, which at the time this work commenced consisted of 2,710,002 unique compounds (version 7). Given that this was the lead like library, a number of filters had previously been applied in order to identify the most lead like compounds from ZINC. Lead structures represent important chemotypes for drug development, ones that are generally pharmacologically active, and consist of simple chemical features amenable to chemical optimisation.33 By utilising considerations put forward to evaluate drug likeness,32 filters were applied to the entirety of ZINC to establish this subset of lead like compounds. Molecules passed if their log P value was greater than or equal to 2.5, but no more than 3.5, whilst their MW was greater than or equal to 250, but not more than 350 Da. These filters were used as the optimisation of low potency leads is often accompanied by an increase in MW and lipophilicity as a consequence of affinity enhancement, thus making µM hits suitable for optimisation.32 Additionally, there are no fewer than 5 and no more than 7 rotatable bonds (RBs) in any of the molecules contained in the lead like library, which is in line with Veber’s guidelines for oral bioavailability.34, 35

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Veber found that reduced molecular flexibility, as measured by the number of RBs, was an important consideration for good oral bioavailability, as was a low polar surface area (PSA). The observations were that compounds which have ten or fewer RBs and a PSA of equal to or less than 140 Å2, have a higher probability of good oral bioavailability. Reduced PSA was also found to correlate better with an increased permeation rate than log P, with an increase in the RB count having a negative effect on the permeation rate. It has also been found that in vitro ligand affinity decreases 0.5 kcal/mol on average for every two RBs.34, 36